| Sr No | Check Points | Remarks |
|---|---|---|
| 1 | Check the proper tightness of Rope | |
| 2 | Check the Lubrication of mechanical movers | |
| 3 | Check the electrical supply in Remote buttons | |
| 4 | Check the stability after providing load | |
| 5 | Check the movements in all possible direction | |
| 6 | Lubricate the sliding parts of Rope | |
| 7 | Check the lock is working properly | |
| 8 | Check the cleanliness of the machine |


Zend Framework
LICENSE
This source file is subject to the new BSD license that is bundled with this package in the file LICENSE.txt. It is also available through the world-wide-web at this URL: http://framework.zend.com/license/new-bsd If you did not receive a copy of the license and are unable to obtain it through the world-wide-web, please send an email to<EMAIL_ADDRESS>so we can send you a copy immediately.
The Zend_Service_SlideShare component is used to interface with the slideshare.net web server to retrieve slide shows hosted on the web site for display or other processing.
SERVICE_UPLOAD_URI
= 'http://www.slideshare.net/api/1/upload_slideshow'
Slide share Web service communication URIs
\Zend_Cache_Core
$_cacheobject= ''
The Cache object to use to perform caching
\Zend_Http_Client
$_httpclient= ''
The HTTP Client object to use to perform requests
__construct(
string $apikey, string $sharedSecret, string $username
=
null, string $password
=
null
)
:
void
The Constructor
| Name | Type | Description |
|---|---|---|
| $apikey | string | The API key |
| $sharedSecret | string | The shared secret |
| $username | string | The username |
| $password | string | The password |
_getSlideShowsByType(
string $key, string $value, int $offset
=
null, int $limit
=
null
)
:
array
Retrieves Zend_Service_SlideShare_SlideShow object arrays based on the type of list desired
| Name | Type | Description |
|---|---|---|
| $key | string | The type of slide show object to retrieve |
| $value | string | The specific search query for the slide show type to look up |
| $offset | int | The offset of the list to start retrieving from |
| $limit | int | The maximum number of slide shows to retrieve |
| Type | Description |
|---|---|
| array | An array of Zend_Service_SlideShare_SlideShow objects |
_slideShowNodeToObject(
\SimpleXMLElement $node
)
:
\Zend_Service_SlideShare_SlideShow
Converts a SimpleXMLElement object representing a response from the service into a Zend_Service_SlideShare_SlideShow object
| Name | Type | Description |
|---|---|---|
| $node | \SimpleXMLElement | The input XML from the slideshare.net service |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare_SlideShow | The resulting object |
getApiKey(
)
:
string
Gets the API key to be used in making API calls
| Type | Description |
|---|---|
| string | the API Key |
getCacheObject(
)
:
\Zend_Cache_Core
Gets the Zend_Cache object which will be used to cache API queries. If no cache object was previously set the the default will be used (Filesystem caching in /tmp with a life time of 43200 seconds)
| Type | Description |
|---|---|
| \Zend_Cache_Core | The object used in caching |
getHttpClient(
)
:
\Zend_Http_Client
Returns the instance of the Zend_Http_Client which will be used. Creates an instance of Zend_Http_Client if no previous client was set.
| Type | Description |
|---|---|
| \Zend_Http_Client | The HTTP client which will be used |
getPassword(
)
:
string
Gets the password to use in API calls
| Type | Description |
|---|---|
| string | the password to use in API calls |
getSharedSecret(
)
:
string
Gets the shared secret used in making API calls
| Type | Description |
|---|---|
| string | the Shared secret |
getSlideShow(
int $ss_id
)
:
\Zend_Service_SlideShare_SlideShow
Retrieves a slide show's information based on slide show ID
| Name | Type | Description |
|---|---|---|
| $ss_id | int | The slide show ID |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare_SlideShow | the Slideshow object |
getSlideShowsByGroup(
string $group, int $offset
=
null, int $limit
=
null
)
:
array
Retrieves an array of slide shows based on group name
| Name | Type | Description |
|---|---|---|
| $group | string | The group name to retrieve slide shows for |
| $offset | int | The offset of the list to start retrieving from |
| $limit | int | The maximum number of slide shows to retrieve |
| Type | Description |
|---|---|
| array | An array of Zend_Service_SlideShare_SlideShow objects |
getSlideShowsByTag(
string $tag, int $offset
=
null, int $limit
=
null
)
:
array
Retrieves an array of slide shows based on tag
| Name | Type | Description |
|---|---|---|
| $tag | string | The tag to retrieve slide shows with |
| $offset | int | The offset of the list to start retrieving from |
| $limit | int | The maximum number of slide shows to retrieve |
| Type | Description |
|---|---|
| array | An array of Zend_Service_SlideShare_SlideShow objects |
getSlideShowsByUsername(
string $username, int $offset
=
null, int $limit
=
null
)
:
array
Retrieves an array of slide shows for a given username
| Name | Type | Description |
|---|---|---|
| $username | string | The username to retrieve slide shows from |
| $offset | int | The offset of the list to start retrieving from |
| $limit | int | The maximum number of slide shows to retrieve |
| Type | Description |
|---|---|
| array | An array of Zend_Service_SlideShare_SlideShow objects |
getUserName(
)
:
string
Returns the user name used for API calls
| Type | Description |
|---|---|
| string | The username |
setApiKey(
string $key
)
:
\Zend_Service_SlideShare
Sets the API key to be used in making API calls
| Name | Type | Description |
|---|---|---|
| $key | string | The API key to use |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare |
setCacheObject(
\Zend_Cache_Core $cacheobject
)
:
\Zend_Service_SlideShare
Sets the Zend_Cache object to use to cache the results of API queries
| Name | Type | Description |
|---|---|---|
| $cacheobject | \Zend_Cache_Core | The Zend_Cache object used |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare |
setHttpClient(
\Zend_Http_Client $client
)
:
\Zend_Service_SlideShare
Sets the Zend_Http_Client object to use in requests. If not provided a default will be used.
| Name | Type | Description |
|---|---|---|
| $client | \Zend_Http_Client | The HTTP client instance to use |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare |
setPassword(
string $pw
)
:
\Zend_Service_SlideShare
Sets the password to use in API calls
| Name | Type | Description |
|---|---|---|
| $pw | string | The password to use |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare |
setSharedSecret(
string $secret
)
:
\Zend_Service_SlideShare
Sets the shared secret used in making API calls
| Name | Type | Description |
|---|---|---|
| $secret | string | the shared secret |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare |
setUserName(
string $un
)
:
\Zend_Service_SlideShare
Sets the user name to use for API calls
| Name | Type | Description |
|---|---|---|
| $un | string | The username to use |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare |
uploadSlideShow(
\Zend_Service_SlideShare_SlideShow $ss, boolean $make_src_public
=
true
)
:
\Zend_Service_SlideShare_SlideShow
Uploads the specified Slide show the the server
| Name | Type | Description |
|---|---|---|
| $ss | \Zend_Service_SlideShare_SlideShow | The slide show object representing the slide show to upload |
| $make_src_public | boolean | Determines if the the slide show's source file is public or not upon upload |
| Type | Description |
|---|---|
| \Zend_Service_SlideShare_SlideShow | The passed Slide show object, with the new assigned ID provided |


Quick Reference
Movement
There are two sets of direction keys:
I O P
K ;
, . /
|
7 8 9
4 6
1 2 3
|
They can be combined with modifier keys like so:
Direction | Walk one step in direction, attack adjacent monster |
Shift-Direction | Run in direction until disturbed |
Alt-Direction | Use last command in that direction |
Other commands
L , Numpad-5 , Enter | Rest one turn, select menu item |
Shift-L , Shift-Numpad-5 | Rest until fully healed or disturbed |
C | Close door |
U | Use item |
D | Drop item |
T | Throw item |
X | Swap—re-equip last unequipped item |
Q | Exit level after finishing quest |
Shift-F | Forfeit level |


AngularJS Testing Examples
AngularJS Testing Examples to
Pisos.com
.
Version: v
Learn more on GitHub »
Home
Operation
Contact


Build
Report
Merge with SEED
Report
×
Compound Concensus:
Build Configuration
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2 writes to DataPacketReceived
System.Management.Automation (2)
engine\remoting\fanin\OutOfProcTransportManager.cs (1)
499
_dataProcessingCallbacks.
DataPacketReceived
+= new OutOfProcessUtils.DataPacketReceived(OnDataPacketReceived);
engine\remoting\server\OutOfProcServerMediator.cs (1)
55
callbacks.
DataPacketReceived
+= new OutOfProcessUtils.DataPacketReceived(OnDataPacketReceived);
1 reference to DataPacketReceived
System.Management.Automation (1)
engine\remoting\fanin\OutOfProcTransportManager.cs (1)
266
callbacks.
DataPacketReceived
(rawData, stream, psGuid);


Presentations
Tutorial:
- Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark, AI conference, Sep 2019, San Jose (slides)
Talks:
-
Context-aware Fast Food Recommendation with Ray on Apache Spark at Burger King, Data + AI Summit Europe 2020, November 2020, (slides)
-
Cluster Serving: Distributed Model Inference using Apache Flink in Analytics Zoo, Flink Forward 2020, October 2020, (slides)
-
Project Zouwu: Scalable AutoML for Telco Time Series Analysis using Ray and Analytics Zoo, Ray Summit Connect 2020, August 2020, (slides)
-
Cluster Serving: Distributed Model Inference using Big Data Streaming in Analytics Zoo, OpML 2020, July 2020, (slides)
-
Scalable AutoML for Time Series Forecasting using Ray, OpML 2020, July 2020, (slides)
-
Scalable AutoML for Time Series Forecasting using Ray, Spark + AI Summit 2020, June 2020, (slides)
-
Running Emerging AI Applications on Big Data Platforms with Ray On Apache Spark, Spark + AI Summit 2020, June 2020, (slides)
-
Vectorized Deep Learning Acceleration from Preprocessing to Inference and Training on Apache Spark in SK Telecom, Spark + AI Summit 2020, June 2020, (slides)
-
Architecture and practice of big data analysis and deep learning model inference using Analytics Zoo on Flink, Flink Forward Asia 2019, Nov 2019, Beijing (slides)
-
Data analysis + AI platform technology and case studies, AICon BJ 2019, Nov 2019, Beijing (slides)
-
Architectural practices for building a unified big data AI application with Analytics-Zoo, QCon SH 2019, Oct 2019, Shanghai (slides)
-
Building AI to play the FIFA video game using distributed TensorFlow, TensorFlow World, Oct 2019, Santa Clara (slides)
-
Deep Learning Pipelines for High Energy Physics using Apache Spark with Distributed Keras on Analytics Zoo, Spark+AI Summit, Oct 2019, Amsterdam (slides)
-
Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction with Geospatial Visualization, Spark+AI Summit, Oct 2019, Amsterdam (slides)
-
LSTM-based time series anomaly detection using Analytics Zoo for Spark and BigDL, Strata Data conference, May 2019, London (slides)
-
Game Playing Using AI on Apache Spark, Spark+AI Summit, April 2019, San Francisco (slides)
-
Using Deep Learning on Apache Spark to Diagnose Thoracic Pathology from Chest X-rays in DELL EMC, Spark+AI Summit, April 2019, San Francisco (slides)
-
Leveraging NLP and Deep Learning for Document Recommendation in the Cloud, Spark+AI Summit, April 2019, San Francisco (slides)
-
Analytics Zoo: Distributed Tensorflow, Keras and BigDL in production on Apache Spark, Strata Data conference, March 2019, San Francisco (slides)
-
User-based real-time product recommendations leveraging deep learning using Analytics Zoo on Apache Spark in Office Depot, Strata Data conference, March 2019, San Francisco (slides)
-
Analytics Zoo: Unifying Big Data Analytics and AI for Apache Spark, Shanghai Apache Spark + AI meetup, Nov 2018, Shanghai (slides)
-
Use Intel Analytics Zoo to build an intelligent QA Bot for Microsoft Azure, Shanghai Apache Spark + AI meetup, Nov 2018, Shanghai (slides)
-
A deep learning approach for precipitation nowcasting with RNN using Analytics Zoo in Cray, Strata Data conference, Sep 2018, New York (slides)
-
Job recommendations leveraging deep learning using Analytics Zoo on Apache Spark in Talroo, Strata Data conference, Sep 2018, New York (slides)
-
Accelerating Deep Learning Training with BigDL and Drizzle on Apache Spark, Spark + AI Summit, June 2018, San Francisco (slides)
-
Using Crowdsourced Images to Create Image Recognition Models with Analytics Zoo in World Bank, Spark + AI Summit, June 2018, San Francisco (slides)
-
Building Deep Reinforcement Learning Applications on Apache Spark with Analytics Zoo using BigDL, Spark + AI Summit, June 2018, San Francisco (slides)
-
Using BigDL on Apache Spark to Improve the MLS Real Estate Search Experience at Scale, Spark + AI Summit, June 2018, San Francisco
-
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL, Spark + AI Summit, June 2018, San Francisco
-
Using Siamese CNNs for removing duplicate entries from real estate listing databases, Strata Data conference, May 2018, London (slides)
-
Classifying images on Spark in World Bank, AI conference, May 2018, New York (slides)
-
Improving user-merchant propensity modeling using neural collaborative filtering and wide and deep models on Spark BigDL in Mastercard, Strata Data conference, March 2018, San Jose (slides)
-
Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling, Strata Data conference, March 2018, San Jose (slides)
-
Automatic 3D MRI knee damage classification with 3D CNN using BigDL on Spark in UCSF, Strata Data conference, March 2018, San Jose (slides)


•
•
•
Sorting
1x1
2x2
3x3
TRACE OF ITEM MOVED FORWARD¹
TRACE OF ITEM MOVED BACKWARD¹
ITEM IN MEMORY
COMPARISON INDICATOR
CURRENT OPERATION
END / SEQUENCE SORTED
MAX NUMBER OF INVERSIONS
STARTING NUMBER OF INVERSIONS
¹COLOR OF TRACE = COLOR OF MOVED ITEM
close
X
+
Add new algorithm
unsorted
backward
play
pause
forward
sorted
Algorithm
(with average case performance)
Color
Size
Initial Condition
Random
Nearly sorted
Reversed
Few unique
+
Visualize algorithm
close
X


| rename_seq {xfun} | R Documentation |
Rename a series of files and add an incremental numeric prefix to the filenames. For example, files ‘a.txt’, ‘b.txt’, and ‘c.txt’ can be renamed to ‘1-a.txt’, ‘2-b.txt’, and ‘3-c.txt’.
rename_seq( pattern = "^[0-9]+-.+[.]Rmd$", format = "auto", replace = TRUE, start = 1, dry_run = TRUE )
pattern |
A regular expression for |
format |
The format for the numeric prefix. This is passed to
|
replace |
Whether to remove existing numeric prefixes in filenames. |
start |
The starting number for the prefix (it can start from 0). |
dry_run |
Whether to not really rename files. To be safe, the default is
|
A named character vector. The names are original filenames, and the vector itself is the new filenames.
xfun::rename_seq() xfun::rename_seq("[.](jpeg|png)$", format = "%04d")


maps and configs
voice chat recordings
text chat logs
videos
in the server, villagers will say random messages.
if you want to make them shitpost, anything you post here will show up in the game.
in game, the substring $player will be replaced with a random player's name.
view other player's suggestions with this api call
source code for the plugin here
Source code for the page and thread watcher here.
You can access the thread watcher's public api here and here.


Scans the input stream provided into a series of tokens.
Examples:
auto scanner = new Scanner(cast(ubyte[])"".representation);
assert(cast(EndOfStream)scanner.nextToken());
scanner = new Scanner(cast(ubyte[])"\n\n\r\n ".representation);
assert(cast(Whitespace)scanner.nextToken());
assert(scanner.lineNumber == 4);
assert(scanner.columnNumber == 3);
scanner = new Scanner(cast(ubyte[])"STOP".representation);
StackOpcode stackToken = cast(StackOpcode)scanner.nextToken();
assert(stackToken.m_opcode == "STOP");
scanner = new Scanner(cast(ubyte[])"PUSH1 0xa".representation);
PushOpcode pushToken = cast(PushOpcode)scanner.nextToken();
assert(pushToken.m_opcode == "PUSH1");
assert(cast(Whitespace)scanner.nextToken());
Number number = cast(Number)scanner.nextToken();
assert(number.m_value == BigInt(10));
scanner = new Scanner(cast(ubyte[])"PUSH1 1234".representation);
pushToken = cast(PushOpcode)scanner.nextToken();
assert(pushToken.m_opcode == "PUSH1");
assert(cast(Whitespace)scanner.nextToken());
number = cast(Number)scanner.nextToken();
assert(number.m_value == BigInt("1234"));
- const @property size_t lineNumber();
- Returns:
The current line number.
Examples:
auto scanner = new Scanner(cast(ubyte[])"\n\n\r\n".representation);
assert(scanner.lineNumber == 1);
- const @property size_t columnNumber();
- Returns:
The current column number.
Examples:
auto scanner = new Scanner(cast(ubyte[])"abcd ".representation);
assert(scanner.columnNumber == 1);
- @property Token currentToken();
- Returns:
The most recent token from the input stream, or null if no
tokens have yet been retrieved.
Examples:
ubyte[] fixture = cast(ubyte[])"PUSH 1".representation;
auto scanner = new Scanner(fixture);
assert(scanner.currentToken is null);
- Token nextToken();
- This routine consumes the input stream, returning the next token found.
If a token could not be found, then an error is raised.
Throws:
InvalidTokenException if an invalid token is encountered.
Returns:
Next token in the input character stream.


About
I help nonprofits, cultural institutions, and good causes win online at Blue State Digital.
This site is hosted on Nearly Free Speech, built with nanoc, and licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. You can check out the source code on Github.


Cookie Coders serves as an organization to enrich the youth through education for the modern world of technology. With our guidance, we will educate the youth on the basics of Java, C++, Python, as well as fun interactive programming such as Fortran 77 and Inform 7. We are hosting a series of 7 day events throughout the United States to target all youth for this great opportunity. Our Game Developer Event offers students the potential of getting a head start in pursuing a career in the booming field of software development.


IntswapsTM is for people who have a variable-rate loan and wish they had a fixed-rate one instead (or vice-versa.) Are you locked into an ARM (adjustable rate mortgage) and deterred by refinancing costs that average $1,500 to $5,000 according to Trulia? IntswapsTM is an easy, inexpensive way to swap your variable rate interest payment for a fixed rate payment on a month by month basis for as many or few monthly payments as you want.
You just post refundable escrow in an Ethereum smart contract of 0.2% ? of the loan principal amount that you want the interest rate to apply to (e.g. $200 for an IntSwapsTM contract on a $100,000 notional loan amount.) The decentralized trust system of the Ethereum Virtual Machine will ensure that you receive your escrow plus your profit or minus your loss back at the end of the swap contract. Interested in learning more? Just go to our no-obligation quote page to see how it would work for you and what your payouts would be in response to potential changes in interest rates.


Your life's already a mess.
Don't let item strings pile on to that wreck.
Prettify From Clipboard
Parsing Item...


Hordeum vulgare (Hordeum vulgare, 030312v2) and Triticum aestivum (Triticum aestivum, IWGSP1) were aligned using the Translated Blat alignment algorithm (Kent W, Genome Res., 2002;12(4):656-64) in Ensembl release 76. Hordeum vulgare was used as the reference species. After running Translated Blat, the raw Translated Blat alignment blocks are chained according to their location in both genomes. During the final netting process, the best sub-chain is chosen in each region on the reference species.
Full list of pairwise alignments| Minimum score (minScore) | 30 |
| Database type (t) | dnax |
| Query type (q) | dnax |
| Mask out repeats (mask) | lower |
| Mask out repeats on query (qmask) | lower |
| Hordeum vulgare | Triticum aestivum | |
|---|---|---|
| Chunk size | 200,100,000 | 200,100,000 |
| Overlap | 10,000 | 10,000 |
| Group set size | 400,100,000 | 400,100,000 |
| Masking options | {default_soft_masking => 1} | {default_soft_masking => 1} |
Number of alignment blocks: 2902234
| Genome coverage(bp) | Coding exon coverage (bp) | |
|---|---|---|
| Hordeum vulgare | ||
| 175,026,891 out of 4,706,173,842 | Matches: 23,008,332 out of 27,670,405 | |
| Uncovered: 4,531,146,951 out of 4,706,173,842 | Mis-matches: 1,480,480 out of 27,670,405 | |
| Insertions: 38,571 out of 27,670,405 | ||
| Uncovered: 3,143,022 out of 27,670,405 | ||
| Triticum aestivum | ||
| 423,284,874 out of 4,460,951,632 | Matches: 70,787,953 out of 85,832,889 | |
| Uncovered: 4,037,666,758 out of 4,460,951,632 | Mis-matches: 4,411,092 out of 85,832,889 | |
| Insertions: 62,541 out of 85,832,889 | ||
| Uncovered: 10,571,303 out of 85,832,889 |


Downloading microscheme
A functional programming language for the Arduino
Pre-requisites
In order to assemble programs and upload them to real Arduinos, you will need some implementation of avr-gcc and avrdude. For most Linux distributions, these tools are available via the standard package managers. They are available via the Homebrew and MacPorts package managers for Mac OS X, and the winavr project on Windows.
Get the latest code
The latest (not necessarily stable) code is always available via GitHub.
Stable Release (v0.9.3)
The latest stable release is available as source code, and as compiled binaries for Linux and Windows. This is the version of microscheme to which the online documentation applies.
Distribution Packages
Microscheme packages are available for the Pacman (Arch Linux) and Nix package managers.
License
The Microscheme source code and binaries are released under the MIT license.


Chicago Crime Apps | IT202
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WITNESS LIST Appropriations-S/C on Health & Human Services Committee (Subcommittee on Health and Human Services) February 12, 2001-8:00A Budget For: Bryant, William (Self) Chavez, Irene (Self) Cranston, Ron (Self) Donovan, Jim (Self) Dunkelberg, Anne (Center for Public Policy Priorities) Evans, Robert (Self) Harracksingh, Rachel (Self) Kafka, Bob (Self) Latimer, David (Texas Association of Homes and Services for the Aging) McPhail, Jennifer (Self) Miller, Judith (Alzheimer's Association Coalition of Texas) Nickerson, Jim (Self) Oliver, William (Self) Perryman, Kevin (Self) Rodgers, Gene (Self) Romberg, Peggy (Women's Health and Family Planning Association) Spahn, Wayne (Self) Spence, Chris (Self) Thomas, Stephanie (Self) Whitten-Lege, Gene (Self) Zoern, Stacy (Self) On: Baum, Jack (Texas Department of Health) Bell, Charles (Texas Department of Health) Blount, Debbie (Texas Department of Health) Gilbert, Don (Health and Human Services Commission) Harriss, Bob (Texas Department of Health) Palmer, David (Texas Department of Health) Speights, Sara (Girling Health Care, Inc.) Stone, Nancy (Self) Willis, John (Self)


SELECT A TABLE
Date and Time
:
Duration
Close
HTML Starting to load
Default Bit String :
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The temporary charge will be refunded on check in
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Please store your Booking ID safely.
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from bs4 import BeautifulSoup
import datetime
import parmed
from pathlib import Path
from urllib.request import Request, urlopen
from urllib.error import URLError
[docs]def dict_from_mmcif(mmcif, path=True):
"""Parse mmcif file into a dictionary.
Notes
-----
Full list of keys/value types, and further information on them can be viewed here:
http://mmcif.wwpdb.org/docs/pdb_to_pdbx_correspondences.html
All values in the returned dict are str or list(str).
This means that some of the data values are string representations of integers
- parse these outside of this function if desired.
An alternative approach to this can be found in Biopython (via the function Bio.PDB.MMCIF2Dict.MMCIF2Dict).
mmcif files are subject to the usual "here be dragons" problems of the PDB and difficult file formats.
As such, this function is likely to be in a permanent state of flux as more dragons are found.
Parameters
----------
mmcif : str
mmcif string or a path to an mmcif file.
path : bool
True if mmcif is a path.
Returns
-------
cif_data : dict
Keys are cif data names, e.g. '_struct_keywords.text'.
Values are str or list(str).
"""
if path:
with open(mmcif, 'r') as foo:
lines = foo.readlines()
else:
lines = mmcif.splitlines()
lines = [' '.join(x.strip().split()) for x in lines]
# Some of the data in a .cif files are stored between 'loop_' to initiate a loop, and '#' to terminate it.
# The variable 'loop' is a flag to keep track of this behaviour.
loop = False
# Set up the dictionary to populate as the lines of the .cif file are iterated over.
cif_data = {}
for i, line in enumerate(lines):
if not line:
continue
# hash signifies end of a loop. Ensure loop flag is set to False.
if line == '#':
loop = False
continue
if not loop:
# This line initiates a loop section, in which keys are listed first,
# followed by lines of data in which the values are listed in the same order as the above keys.
# The values in the loop section will be stored as lists - there are multiple values for one key.
# An example of this type of data is the 'REVDAT' section, which stores details on the (potentially
# numerous) various revisions made to the PDB file during its history.
if line[:5] == 'loop_':
loop = True
key_list = []
continue
# Lines beginning with '_' start with data names, i.e. keys in the cif_data dictionary.
elif line[0] == '_':
# If line consists only of a key, then subsequent lines may contain the associated value.
if len(line.split()) == 1:
current_key = line
count = 1
while True:
# Look forward until a key is found, keeping count of the number of lines in between.
try:
if lines[i + count][0] != '_':
count += 1
# prevent infinite loop.
elif i + count > len(lines):
break
else:
if count > 1:
try:
cif_data[current_key] = ' '.join(lines[i + 1: i + count])
except IndexError:
cif_data[current_key] = None
else:
cif_data[current_key] = None
break
except IndexError:
break
continue
# Simplest case. Line is a key-value pair, with the key identified by its first character, '_'.
elif len(line.split()) > 1:
line = line.split()
try:
cif_data[line[0]] = ' '.join(line[1:])
except IndexError:
cif_data[line[0]] = None
continue
# Line is one of multiple lines that are combined into a value in the while True: loop above.
else:
continue
else:
# Within a loop section, keys are identified by their first character '_'.
# Add them to the list of keys in the loop.
if line[0] == '_':
if len(line.split()) == 1:
key_list.append(line)
if line not in cif_data.keys():
cif_data[line] = []
# Within a loop section, the values are listed within a single space-separated line in the same order
# that the keys were listed at the start of the loop.
else:
# Cannot do a simple split if any of the values themselves are strings containing at least one space.
if '\"' in line and line.count('\"') % 2 == 0:
line_parts = [x.strip() for x in line.split('\"') if x]
line = []
for part in line_parts:
if line_parts.index(part) % 2 == 0:
for x in part.split():
line.append(x)
else:
line.append(part)
elif '\'' in line and line.count('\'') % 2 == 0:
line = [x.strip() for x in line.split('\'') if x]
elif len(key_list) == len(line.split()):
line = line.split()
if len(key_list) == len(line):
for j, v in enumerate(line):
cif_data[key_list[j]].append(line[j])
else:
# CURRENTLY THERE IS A PROBLEM WITH REALLY LONG LOOPS eg _pdbx_refine_tls*, _pdbx_struct_oper_list*
# The values span multiple lines, and therefore do not satisfy
# the condition of the above 'if' statement.
# A correction for this needs to keep track of the value count on subsequent lines,
# until the 'if' condition is met.
continue
return cif_data
[docs]def get_protein_dict(cif_data):
""" Parse cif_data dict for a subset of its data.
Notes
-----
cif_data dict contains all the data from the .cif file, with values as strings.
This function returns a more 'human readable' dictionary of key-value pairs.
The keys have simpler (and still often more descriptive!) names, and the values are not restricted to being strings.
To add more key-value pairs to the protein_dict, follow the patterns used in this function.
Add the key and youre name for it to mmcif_data_names.
Will it need further parsing, like with the dates in the function below?
If the value is not a string, add it to a list of data-types at the end of the function.
More information on what key-value pairs can be obtained can be gleaned by examining cif_data and/or by viewing the
mmcif resource on the PDB website: http://mmcif.wwpdb.org/docs/pdb_to_pdbx_correspondences.html
WARNING: Do not alter the keys of protein_dict without caution.
The keys of protein_dict MUST match the column names of the Protein model in the protgraph database.
Parameters
----------
cif_data : dict
Key/value pairs taken directly from a .cif file.
Output of the function dict_from_mmcif.
Returns
-------
protein_dict : dict
A dictionary containing a parsed subset of the data in cif_data.
The keys have the same name as fields in the Protein model.
"""
# Dictionary relating the keys of protein_dict (column names in Protein model) to the keys of cif_data.
mmcif_data_names = {
'keywords': '_struct_keywords.text',
'header': '_struct_keywords.pdbx_keywords',
'space_group': '_symmetry.space_group_name_H-M',
'experimental_method': '_exptl.method',
'crystal_growth': '_exptl_crystal_grow.pdbx_details',
'resolution': '_refine.ls_d_res_high',
'r_value_obs': '_refine.ls_R_factor_obs',
'atoms_protein': '_refine_hist.pdbx_number_atoms_protein',
'atoms_solvent': '_refine_hist.number_atoms_solvent',
'atoms_ligand': '_refine_hist.pdbx_number_atoms_ligand',
'atoms_nucleic_acid': '_refine_hist.pdbx_number_atoms_nucleic_acid',
'atoms_total': '_refine_hist.number_atoms_total',
'title': '_struct.title',
'pdb_descriptor': '_struct.pdbx_descriptor',
'model_details': '_struct.pdbx_model_details',
'casp_flag': '_struct.pdbx_CASP_flag',
'model_type_details': '_struct.pdbx_model_type_details',
'ncbi_taxonomy': '_entity_src_nat.pdbx_ncbi_taxonomy_id',
'ncbi_taxonomy_gene': '_entity_src_gen.pdbx_gene_src_ncbi_taxonomy_id',
'ncbi_taxonomy_host_org': '_entity_src_gen.pdbx_host_org_ncbi_taxonomy_id',
}
# Set up initial protein_dict.
protein_dict = {}
for column_name, cif_name in mmcif_data_names.items():
try:
data = cif_data[cif_name]
except IndexError:
data = None
except KeyError:
data = None
protein_dict[column_name] = data
# These entries are modified from the mmcif dictionary.
# There may be many revision dates in cif_data. We save the original deposition, release and last_modified dates.
# If there are many dates, they will be in a list in cif_data, otherwise it's one date in a string
# Is there a tidier way to do this?
if isinstance(cif_data['_database_PDB_rev.date_original'], str):
protein_dict['deposition_date'] = cif_data['_database_PDB_rev.date_original']
else:
protein_dict['deposition_date'] = cif_data['_database_PDB_rev.date_original'][0]
if isinstance(cif_data['_database_PDB_rev.date'], str):
protein_dict['release_date'] = cif_data['_database_PDB_rev.date']
protein_dict['last_modified_date'] = cif_data['_database_PDB_rev.date']
else:
protein_dict['release_date'] = cif_data['_database_PDB_rev.date'][0]
protein_dict['last_modified_date'] = cif_data['_database_PDB_rev.date'][-1]
# crystal_growth should be a string or None
crystal_growth = protein_dict['crystal_growth']
if type(crystal_growth) == list and len(crystal_growth) >= 1:
protein_dict['crystal_growth'] = crystal_growth[0]
else:
protein_dict['crystal_growth'] = None
# taxonomy data types should be ints, not lists
taxonomy_keys = ['ncbi_taxonomy', 'ncbi_taxonomy_gene', 'ncbi_taxonomy_host_org']
for taxonomy_key in taxonomy_keys:
if protein_dict[taxonomy_key]:
if type(protein_dict[taxonomy_key]) == list:
try:
protein_dict[taxonomy_key] = int(protein_dict[taxonomy_key][0])
except ValueError or IndexError:
protein_dict[taxonomy_key] = None
# Convert data types from strings to their correct data type.
ints = ['atoms_ligand', 'atoms_nucleic_acid', 'atoms_protein', 'atoms_solvent', 'atoms_total']
floats = ['r_value_obs', 'resolution']
dates = ['deposition_date', 'release_date', 'last_modified_date']
for k, v in protein_dict.items():
if v:
if v == '?' or v == 'None' or v == '.':
protein_dict[k] = None
elif k in ints:
protein_dict[k] = int(v)
elif k in floats:
protein_dict[k] = float(v)
elif k in dates:
protein_dict[k] = datetime.datetime.strptime(v, '%Y-%m-%d')
# Parse awkward strings from cif_data.
elif type(v) == str:
v = v.replace('loop_', '')
v = v.replace(' # ', '')
if v[0] == v[-1] == '\'':
protein_dict[k] = v[1:-1]
return protein_dict
[docs]def get_protein_dict_from_parmed(cif_file):
s = parmed.formats.CIFFile.parse(cif_file, skip_bonds=True)
protein_dict = {
'keywords': s.keywords,
'space group': s.space_group,
'experimental_method': s.experimental,
'resolution': s.resolution,
'atoms_total': len(s.atoms),
'title': s.title,
'journal': s.journal,
'authors': s.authors,
'journal_year': s.year,
'journal_volume': s.volume,
'journal_volume_page': s.volume_page,
'journal_page': s.page,
'doi': s.doi,
}
return protein_dict
[docs]def parse_PISCES_output(pisces_output, path=False):
""" Takes the output list of a PISCES cull and returns in a usable dictionary.
Notes
-----
Designed for outputs of protein sequence redundancy culls conducted using the PISCES server.
http://dunbrack.fccc.edu/PISCES.php
G. Wang and R. L. Dunbrack, Jr. PISCES: a protein sequence culling server. Bioinformatics, 19:1589-1591, 2003.
Parameters
----------
pisces_output : str or path
Output list of non-redundant protein chains from PISCES, or path to text file.
path : bool
True if path given rather than string.
Returns
-------
pisces_dict : dict
Data output by PISCES in dictionary form.
"""
pisces_dict = {}
if path:
pisces_path = Path(pisces_output)
pisces_content = pisces_path.read_text().splitlines()[1:]
else:
pisces_content = pisces_output.splitlines()[1:]
for line in pisces_content:
pdb = line.split()[0][:4].lower()
chain = line.split()[0][-1]
pdb_dict = {'length': line.split()[1],
'method': line.split()[2],
'resolution': line.split()[3],
'R-factor': line.split()[4],
'R-free': line.split()[5]}
if pdb in pisces_dict:
pisces_dict[pdb]['chains'].append(chain)
else:
pdb_dict['chains'] = [chain]
pisces_dict[pdb] = pdb_dict
return pisces_dict
[docs]def download_decode(URL, encoding='utf-8', verbose=True):
""" Downloads data from URL and returns decoded contents."""
if verbose:
print("Downloading data from " + URL)
req = Request(URL)
try:
with urlopen(req) as u:
decoded_file = u.read().decode(encoding)
except URLError as e:
if hasattr(e, 'reason'):
print('Server could not be reached.')
print('Reason: ', e.reason)
elif hasattr(e, 'code'):
print('The server couldn\'t fulfill the request.')
print('Error code: ', e.code)
return None
return decoded_file
[docs]def olderado_best_model(pdb_id):
""" Checks the Olderado web server and returns the most representative conformation for PDB NMR structures.
Notes
-----
Uses OLDERADO from the EBI.
See http://www.ebi.ac.uk/pdbe/nmr/olderado/ and citations therein.
Parameters
----------
pdb_id : str
The 4-character PDB code for the NMR structure of interest.
Returns
-------
model_no : int
The conformation number of the most-representative conformation.
Raises
------
ValueError
If the model number it finds is not an integer. This might indicate that the website format has changed.
"""
pdb_code = pdb_id[:4].lower()
olderado_url = 'http://www.ebi.ac.uk/pdbe/nmr/olderado/searchEntry?pdbCode=' + pdb_code
olderado_page = download_decode(olderado_url, verbose=False)
if olderado_page:
parsed_page = BeautifulSoup(olderado_page, 'html.parser')
else:
return None
try:
best_model = parsed_page.find_all('td')[1]
except IndexError:
print("No model info could be found for {0} - ensure that it's an NMR structure.".format(pdb_id))
return None
try:
model_no = int(best_model.string)
except ValueError as v:
print("Did not find a number for best model.")
raise v
return model_no
__author__ = 'Jack W. Heal, Kieran L. Hudson'


--- # jekyll header --- {% include header.html %} {% include container-start.html %}
This is the CI build of The Argonaut Data Query Implementation Guide For list of available versions, see the Directory of published versions .
{% capture my-include %}{% include intro.md %}{% endcapture %}{{ my-include | markdownify }} {% include container-end.html %} {% include footer.html %}


Video: Redskins Fans Send Message To Owner Daniel Snyder
by Tzvi MachlinThe Washington Redskins are in the waning minutes of a blowout loss to the New York Jets. At 1-8 on the season – soon to be 1-9 – the team has long-since been knocked out of any realistic playoff contention.
But after firing head coach Jay Gruden earlier this year, the Washington Redskins fans have turned their anger towards the man at the top: Owner Daniel Snyder.
In the middle of the game, with Washington struggling mightily on offense, fans began chanting “Sell the team!”
A loss today will secure the third-straight losing season for Washington, and their eighth in the last 11 years.
As bad as the 2019 season has been going for Washington, many fans might consider it a microcosm of Daniel Snyder’s entire run as owner.
Since buying the team prior to the 1999 season, Washington has made just five playoff appearances and struggled to find any sustained success as a franchise.
That tenure has also been marred by the slow deterioration of their home at FedEx Field. Despite multiple attempts to renovate and expand the stadium, it’s still widely considered one of the worst stadiums in the country.
Washington is a proud franchise with a long history. While they can never force Snyder to sell the team, they can make their team’s owner hurt in the pockets if they want to take their frustrations to the next level.


All of us on this team are Lambda students aswell! This means that we know exactly what you are looking to accomplish, as we have been there, and we will do whatever we can to resolve your issues. We want you to succeed, and will use all of our resources to make sure that happens.
A brief description from each of our team members:
Mick is a student and team leader at Lambda and is working the front-end development for this build.
Follow @mgroff1Tyler is a team leader at Lambda and is taking on the project manager position for this build.
Follow @Tyler668Mark is a student at Lambda and is working the backend development side of this build.
Follow @mark-hallsCarl is a student at Lambda and is working the marketing web development for this build.
Follow @carlsachsShaqib is a student at Lambda and is working on front-end development for this build.
Follow @Skeeb32


{% extends '../layouts/template.html' %} {% block title %}Check your State Pension - {{mvpversion}}{% endblock %} {% block serviceName %}Check your State Pension{% endblock %} {% block main %}
You’ll reach State Pension age on 1 December 2020 when you’ll be 67.
By this time, you will not have the 10 years needed on your National Insurance record to get any State Pension.
If you have lived or worked overseas (opens in new tab), you may be able to use your time abroad to make your years in the UK qualify for the purposes of getting any State Pension.
You also have 5 years on your record which do not count towards your pension because you did not contribute enough National Insurance. Filling some of these years may get you some State Pension.
Gaps in your record and the cost of filling them
After State Pension age, 1 December 2020 you no longer pay National Insurance contributions.
You may have a private pension. Use the free service to find pension contact details (opens in new tab).
You may be eligible for Pension Credit (opens in new tab) if your retirement income is low.
Contact the Money Advice Service (opens in new tab) for free impartial advice.


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Far far away, behind the word mountains, far from the country Vokalia, there live the blind texts
Les merFar far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Advokatfullmektig
Advokatfullmektig


I offered to provide Mr Douglas with a child, a bottle of prussic acid, a copy of The Well Of Loneliness, and—if he kept his word and chose to administer the acid—a handsome memorial in marble to be erected wherever he might appoint, after his execution. The offer, I regret to say, was not accepted.” — Aldous Huxley, Music At Night & Other Essays


{% extends 'core/base.html' %} {% block content %}
Our contact information can be found on Garnet Gate. Students can log in to see more details such as meeting times and events.


This section defines the rules for MPI language binding in general and for Fortran, ANSI C, and C++, in particular. (Note that ANSI C has been replaced by ISO C. References in MPI to ANSI C now mean ISO C.) Defined here are various object representations, as well as the naming conventions used for expressing this standard. The actual calling sequences are defined elsewhere.
MPI bindings are for Fortran 90, though they are designed to be usable in Fortran 77 environments.
Since the word PARAMETER is a keyword in the Fortran language, we use the word ``argument'' to denote the arguments to a subroutine. These are normally referred to as parameters in C and C++, however, we expect that C and C++ programmers will understand the word ``argument'' (which has no specific meaning in C/C++), thus allowing us to avoid unnecessary confusion for Fortran programmers.
Since Fortran is case insensitive, linkers may use either lower case or upper case when resolving Fortran names. Users of case sensitive languages should avoid the ``mpi_'' and ``pmpi_'' prefixes.


 While occasionally reasonable, this is often due to a poor naming scheme, and may be redundant and
annoying.
Example:
package byteCode;
class ByteCodeAnalyzer {}
Quick-fix to rename the class is suggested only in the editor.


{% extends 'layout.html' %} {% block pageTitle %} NHS login - Allow someone to enter {{data.service or 'stepWell'}} on your behalf {% endblock %} {% block content %}
To allow someone else to act on your behalf, you will need to tell us their:
You must have the person’s permission to use their information.
Adding someone to {{ data.service or 'stepWell' }} means they will be able to act on your behalf and see your information within {{ data.service or 'stepWell' }}.
On your behalf, they will be able to:
They will not be able to see your medical records.


Tasks Part 2 for
Energy Smart Home Lab GUI (ESHL GUI)
start tasks
P.S.: Each task has a time limit. When the time is up, it will automatically jump to the next task.
The purpose of this is to make sure you have time to go through all the tasks, so take it easy.
If you finish the task before the end of time, please click the button NEXT to continue the next task.


This is just filler content to space the two sticky bars.
To use the toast popup functionality the fusionCSS.js library needs to be included.
This is just filler content to space the two sticky bars.

This is a sticky bar

This is just filler content to space the two sticky bars.

This is a 2nd sticky bar

$(document).ready(function() {
$('.stickyBar').stickyOnScroll({
stoppedBy: $('.appbar')
});
$('.stickyBar2').stickyOnScroll({
stoppedBy: $('.stickyBar')
});
});
Parameters


Cops make R1.1m tik drug bust at Century City apartment
by Jenna EtheridgeA man was arrested in Century City, Milnerton after police discovered just over 3kg of tik (methamphetamine) at an apartment, Western Cape police said.
The drugs had an estimated street value of R1.1m, said Colonel Andrè Traut.
READ | K9 unit busts alleged tik, mandrax dealer in Paarl
Officers had searched the apartment on Saturday afternoon after receiving information that the suspected drug dealer was using the address as a storage facility.
They also found a substantial amount of money in different currencies, which was confiscated.
The man, 49, was expected to appear in court on Monday.


{% load staticfiles %}
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The LinkX language file allows you to translate error and other informative messages into your native language. This document describes how to modify the language file settings.
Control Panel Editing Interface
All changes to your language settings will be made through the control panel. To edit your language settings access the Templates > Language File function from the control panel menu. A page will load where you can edit all of the available language settings. Each of the items should be self-explanitory by reading the existing text. You can modify the settings to change the messages or translate them into your language. Once you have made the changes you want, press the Save Language File button at the top of the page.
Value Placeholders
Some of the default messages have value placeholders (%d, %s, etc) embedded in them so that dynamic information can be inserted into them. Each of the language file settings that has these placeholder values are described below.
| RATING_RANGE | %d will be replaced by the maximum rating value you specified in the General Settings interface |
| BLACKLIST_MATCHED |
The first %s will be the blacklist item that was matched The second %s will be the reason why this item is in the blacklist |
| TITLE_LENGTH |
The first %d will be the minimum title length The second %d will be the maximum title length |
| COMMENT_LENGTH |
The first %d will be the minimum comment length The second %d will be the maximum comment length |
| DESCRIPTION_LENGTH |
The first %d will be the minimum description length The second %d will be the maximum description length |
| MAXIMUM_KEYWORDS | %d will be replaced by the maximum keywords value you specified in the General Settings interface |
| BROKEN_URL |
The first %s is the type of URL submitted (either site URL or recip URL) The second %s is the error message associated with this URL |
| COMMENT_LIMIT | %d is the number of seconds a user must wait between comment submissions |


--- layout: post title: |- Weather Impact? RT ClimateWatch Magazine » Wind Turbines Churn the Air over the North Sea date: '2011-06-17T16:12:00.000-04:00' author: Bob Sica tags: - wind turbines - lidar - energy modified_time: '2011-12-21T12:48:50.252-05:00' blogger_id: tag:blogger.com,1999:blog-736339229338318027.post-1860760935156082993 blogger_orig_url: http://blog.purplecrowlidar.ca/2011/06/weather-impact-rt-climatewatch-magazine.html ---
Wind turbines on the North Sea generate clouds in their wake. It is an open question some researchers are investigating, if we build significant numbers of these structures, particularly inland, can their drag on the atmosphere impact weather patterns? Nothing's free, huh?


 Turducken andouille shoulder, drumstick venison pastrami spare ribs frankfurter meatloaf shank sirloin beef ribs strip steak.
 Turducken andouille shoulder, drumstick venison pastrami spare ribs frankfurter meatloaf shank sirloin beef ribs strip steak.
Executing on a consistent theme whilst coming up with templates to suit all can be a tricky task. Luckily Colorfrog has it all covered.
 Turducken andouille shoulder, drumstick venison pastrami spare ribs frankfurter meatloaf shank sirloin beef ribs strip steak.
 Turducken andouille shoulder, drumstick venison pastrami spare ribs frankfurter meatloaf shank sirloin beef ribs strip steak.
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{% if TransactionSuccess != None %}
{% if TransactionSuccess == True %}
Success!
{{ TransactionMessage }}
{% else %}
Oops, that didn't work!
{{ TransactionMessage }}
{% endif %}
{% endif %}
Place a bid for a new item
You can place a bid here for a new item that hasn't been listed by sellers yet. If a seller is
willing to sell the item you want at or below your price, we will charge you and connect you to
the seller.


At Voices of Carmel, we are very grateful for our community and the services it provides and we want nothing more than to give back to it. We believe the best way to do this is by creating, organizing, and spreading awareness for volunteering opportunities across Carmel. If you have any volunteering opportunites or are intersted in volunteering in Carmel Voices of Carmel Volunteering has you covered. With Voices of Carmel Volunteering, we hope to make a real impact on this amazing city and help as many people as possible at the same time. We hope you take advantage of this resource brought to you by NexEdge Innovations.
September 2020
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Primary pet tenant
Scobie is a 7.5 year old neutered Jack Russel Terrier/Pug mix. Hannah adopted Scobie over 3 years ago and he has been living in apartment rentals up to the point when Hannah purchased her home in 2016.
Scobie is 15 lbs and up-to-date with all of his shots. He is very social and playful and loves befriending other dogs, children and adults. Scobie is 100% potty trained and enjoys getting his energy out with a walk, a trip to the dog park or a good game of fetch.


Our website templates are created with inspiration, checked for quality and originality and meticulously sliced and coded. What's more, they're absolutely free! You can do a lot with them. You can modify them. You can use them to design websites for clients, so long as you agree with the. You can even remove all our links if you want to.
Looking for more templates? Just browse through all our . and find what you're looking for. But if you don't find any website template you can use, you can try our .service and tell us all about it. Maybe you're looking for something different, something special. And we love the challenge of doing something different and something special.
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Want to change peoples mindsets, then give a pitch on how best to make things fun!!


#assign uuid="true">
UUID has native support in the following database platforms:
When using these database platforms UUID is stored in a native manor.
Stored as BINARY(16), has the option of storing in optimised manor meaning the order of bytes is changed to provide a better distribution in index.
Stored as VARCHAR(40).
When the database platform does not support UUID natively we can configure the
storage option via DatabaseConfig.setDbUuid(...)


Wallet
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interface SuspendIterator<out V>
Iterator that suspends its functions. Instances of this interface are not thread-safe and shall not be used from concurrent coroutines.
Author
Davide Farella
hasNext |
Returns abstract suspend operator fun hasNext(): Boolean |
next |
Retrieves the element next available element. next should only be used in pair with hasNext: abstract suspend operator fun next(): V |


GeniBlocks Examples
Geniverse 2.0 Prototype
Case Log
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Genome
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Chromosomes
Genome Test
Experiments
Create ten
Create ten (hidden)
Meiosis
Meiosis with Gamete Filtering
Draggable Alleles
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Welcome to my blog! A lot of posts will be published soon. You can follow my schedule there and my first post will be online on Friday.
Stay tuned and happy reading 😎
Welcome to my blog! A lot of posts will be published soon. You can follow my schedule there and my first post will be online on Friday.
Stay tuned and happy reading 😎


Chapter 3¶
This is the third in a series of notebooks related to astronomy data.
As a running example, we are replicating parts of the analysis in a recent paper, “Off the beaten path: Gaia reveals GD-1 stars outside of the main stream” by Adrian M. Price-Whelan and Ana Bonaca.
In the first lesson, we wrote ADQL queries and used them to select and download data from the Gaia server.
In the second lesson, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.
Now we’ll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1.
Outline¶
Here are the steps in this lesson:
We’ll read back the results from the previous lesson, which we saved in a FITS file.
Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
We’ll put those results into a Pandas
DataFrame
, which we’ll use to select stars near the centerline of GD-1.Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD-1.
Finally, we’ll select and plot the stars whose proper motion is in that region.
After completing this lesson, you should be able to
Select rows and columns from an Astropy
Table
.Use Matplotlib to make a scatter plot.
Use Gala to transform coordinates.
Make a Pandas
DataFrame
and use a BooleanSeries
to select rows.Save a
DataFrame
in an HDF5 file.
Installing libraries¶
If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we’ll use.
If you are running this notebook on your own computer, you might have to install these libraries yourself.
If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.
TODO: Add a link to the instructions.
# If we're running on Colab, install libraries
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install astroquery astro-gala pyia python-wget
Reload the data¶
In the previous lesson, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars. We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.
If you ran the previous lesson successfully, you should already have a file called gd1_results.fits
that contains the data we downloaded.
If not, you can run the following cell, which downloads the data from our repository.
import os
from wget import download
filename = 'gd1_results.fits'
path = 'https://github.com/AllenDowney/AstronomicalData/raw/main/data/'
if not os.path.exists(filename):
print(download(path+filename))
Now here’s how we can read the data from the file back into an Astropy Table
:
from astropy.table import Table
results = Table.read(filename)
The result is an Astropy Table
.
We can use info
to refresh our memory of the contents.
results.info
<Table length=140340>
name dtype unit description
--------------- ------- -------- ------------------------------------------------------------------
source_id int64 Unique source identifier (unique within a particular Data Release)
ra float64 deg Right ascension
dec float64 deg Declination
pmra float64 mas / yr Proper motion in right ascension direction
pmdec float64 mas / yr Proper motion in declination direction
parallax float64 mas Parallax
parallax_error float64 mas Standard error of parallax
radial_velocity float64 km / s Radial velocity
Selecting rows and columns¶
In this section we’ll see operations for selecting columns and rows from an Astropy Table
. You can find more information about these operations in the Astropy documentation.
We can get the names of the columns like this:
results.colnames
['source_id',
'ra',
'dec',
'pmra',
'pmdec',
'parallax',
'parallax_error',
'radial_velocity']
And select an individual column like this:
results['ra']
|<PHONE_NUMBER>5991023 |
|<PHONE_NUMBER>1346344 |
|<PHONE_NUMBER>7468074 |
|<PHONE_NUMBER>0926034 |
|<PHONE_NUMBER>4478618 |
|<PHONE_NUMBER>8999614 |
|<PHONE_NUMBER>1317677 |
|<PHONE_NUMBER>464589 |
|<PHONE_NUMBER>5823267 |
|<PHONE_NUMBER>2869012 |
|<PHONE_NUMBER>768316 |
|<PHONE_NUMBER>6534987 |
| ... |
|<PHONE_NUMBER>7172972 |
|<PHONE_NUMBER>516182 |
|<PHONE_NUMBER>8919313 |
|<PHONE_NUMBER>3215973 |
|<PHONE_NUMBER>1662764 |
|<PHONE_NUMBER>47594 |
|<PHONE_NUMBER>0557786 |
|<PHONE_NUMBER>695258 |
|<PHONE_NUMBER>8686904 |
|<PHONE_NUMBER>5481808 |
|<PHONE_NUMBER>159247 |
|<PHONE_NUMBER>295401 |
The result is a Column
object that contains the data, and also the data type, units, and name of the column.
type(results['ra'])
astropy.table.column.Column
The rows in the Table
are numbered from 0 to n-1
, where n
is the number of rows. We can select the first row like this:
results[0]
| source_id | ra | dec | pmra | pmdec | parallax | parallax_error | radial_velocity |
|---|---|---|---|---|---|---|---|
| deg | deg | mas / yr | mas / yr | mas | mas | km / s | |
| int64 | float64 | float64 | float64 | float64 | float64 | float64 | float64 |
|<PHONE_NUMBER>86749568 |<PHONE_NUMBER>5991023 | 21.75771616932985 | -2.5168384683875766 | 2.941813096629439 | -0.2573448962333354 | 0.823720794509811 | 1e+20 |
As you might have guessed, the result is a Row
object.
type(results[0])
astropy.table.row.Row
Notice that the bracket operator selects both columns and rows. You might wonder how it knows which to select.
If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.
If you apply the bracket operator twice, you can select a column and then an element from the column.
results['ra'][0]
142.48301935991023
Or you can select a row and then an element from the row.
results[0]['ra']
142.48301935991023
You get the same result either way.
Scatter plot¶
To see what the results look like, we’ll use a scatter plot. The library we’ll use is Matplotlib, which is the most widely-used plotting library for Python.
The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.
We’ll import like this.
import matplotlib.pyplot as plt
Pyplot part of the Matplotlib library. It is conventional to import it using the shortened name plt
.
Pyplot provides two functions that can make scatterplots, plt.scatter and plt.plot.
scatter
is more versatile; for example, you can make every point in a scatter plot a different color.plot
is more limited, but for simple cases, it can be substantially faster.
Jake Vanderplas explains these differences in The Python Data Science Handbook
Since we are plotting more than 100,000 points and they are all the same size and color, we’ll use plot
.
Here’s a scatter plot with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees.
x = results['ra']
y = results['dec']
plt.plot(x, y, 'ko')
plt.xlabel('ra (degree ICRS)')
plt.ylabel('dec (degree ICRS)');
The arguments to plt.plot
are x
, y
, and a string that specifies the style. In this case, the letters ko
indicate that we want a black, round marker (k
is for black because b
is for blue).
The functions xlabel
and ylabel
put labels on the axes.
This scatter plot has a problem. It is “overplotted”, which means that there are so many overlapping points, we can’t distinguish between high and low density areas.
To fix this, we can provide optional arguments to control the size and transparency of the points.
Exercise: In the call to plt.plot
, add the keyword argument markersize=0.1
to make the markers smaller.
Then add the argument alpha=0.1
to make the markers nearly transparent.
Adjust these arguments until you think the figure shows the data most clearly.
Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars. These stripes are caused by the way Gaia scans the sky, which you can read about here. The dataset we are using, Gaia Data Release 2, covers 22 months of observations; during this time, some parts of the sky were scanned more than others.
Transform back¶
Remember that we selected data from a rectangle of coordinates in the GD1Koposov10
frame, then transformed them to ICRS when we constructed the query.
The coordinates in results
are in ICRS.
To plot them, we will transform them back to the GD1Koposov10
frame; that way, the axes of the figure are aligned with the GD-1, which will make it easy to select stars near the centerline of the stream.
To do that, we’ll put the results into a GaiaData
object, provided by the pyia library.
from pyia import GaiaData
gaia_data = GaiaData(results)
type(gaia_data)
pyia.data.GaiaData
Now we can extract sky coordinates from the GaiaData
object, like this:
import astropy.units as u
skycoord = gaia_data.get_skycoord(
distance=8*u.kpc,
radial_velocity=0*u.km/u.s)
We provide distance
and radial_velocity
to prepare the data for reflex correction, which we explain below.
type(skycoord)
astropy.coordinates.sky_coordinate.SkyCoord
The result is an Astropy SkyCoord
object (documentation here), which provides transform_to
, so we can transform the coordinates to other frames.
import gala.coordinates as gc
transformed = skycoord.transform_to(gc.GD1Koposov10)
type(transformed)
astropy.coordinates.sky_coordinate.SkyCoord
The result is another SkyCoord
object, now in the GD1Koposov10
frame.
The next step is to correct the proper motion measurements from Gaia for reflex due to the motion of our solar system around the Galactic center.
When we created skycoord
, we provided distance
and radial_velocity
as arguments, which means we ignore the measurements provided by Gaia and replace them with these fixed values.
That might seem like a strange thing to do, but here’s the motivation:
Because the stars in GD-1 are so far away, the distance estimates we get from Gaia, which are based on parallax, are not very precise. So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc. See Koposov, Rix, and Hogg, 2010.
For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct. But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.
The measurement of radial velocity has no effect on the correction for proper motion; the value we provide is arbitrary, but we have to provide a value to avoid errors in the reflex correction calculation.
We are grateful to Adrian Price-Whelen for his help explaining this step in the analysis.
With this preparation, we can use reflex_correct
from Gala (documentation here) to correct for solar reflex motion.
gd1_coord = gc.reflex_correct(transformed)
type(gd1_coord)
astropy.coordinates.sky_coordinate.SkyCoord
The result is a SkyCoord
object that contains
The transformed coordinates as attributes named
phi1
andphi2
, which represent right ascension and declination in theGD1Koposov10
frame.The transformed and corrected proper motions as
pm_phi1_cosphi2
andpm_phi2
.
We can select the coordinates like this:
phi1 = gd1_coord.phi1
phi2 = gd1_coord.phi2
And plot them like this:
plt.plot(phi1, phi2, 'ko', markersize=0.1, alpha=0.2)
plt.xlabel('ra (degree GD1)')
plt.ylabel('dec (degree GD1)');
Remember that we started with a rectangle in GD-1 coordinates. When transformed to ICRS, it’s a non-rectangular polygon. Now that we have transformed back to GD-1 coordinates, it’s a rectangle again.
Pandas DataFrame¶
At this point we have three objects containing different subsets of the data.
type(results)
astropy.table.table.Table
type(gaia_data)
pyia.data.GaiaData
type(gd1_coord)
astropy.coordinates.sky_coordinate.SkyCoord
On one hand, this makes sense, since each object provides different capabilities. But working with three different object types can be awkward.
It will be more convenient to choose one object and get all of the data into it. We’ll use a Pandas DataFrame, for two reasons:
It provides capabilities that are pretty much a superset of the other data structures, so it’s the all-in-one solution.
Pandas is a general-purpose tool that is useful in many domains, especially data science. If you are going to develop expertise in one tool, Pandas is a good choice.
However, compared to an Astropy Table
, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns.
It’s easy to convert a Table
to a Pandas DataFrame
.
import pandas as pd
df = results.to_pandas()
df.shape
(140340, 8)
DataFrame
provides shape
, which shows the number of rows and columns.
It also provides head
, which displays the first few rows. It is useful for spot-checking large results as you go along.
df.head()
| source_id | ra | dec | pmra | pmdec | parallax | parallax_error | radial_velocity | |
|---|---|---|---|---|---|---|---|---|
| 0 |<PHONE_NUMBER>86749568 | 142.483019 | 21.757716 | -2.516838 | 2.941813 | -0.257345 | 0.823721 | 1.000000e+20 |
| 1 |<PHONE_NUMBER>17112960 | 142.254529 | 22.476168 | 2.662702 | -12.165984 | 0.422728 | 0.297472 | 1.000000e+20 |
| 2 |<PHONE_NUMBER>68978688 | 142.645286 | 22.166932 | 18.306747 | -7.950660 | 0.103640 | 0.544584 | 1.000000e+20 |
| 3 |<PHONE_NUMBER>75786752 | 142.577394 | 22.227920 | 0.987786 | -2.584105 | -0.857327 | 1.059607 | 1.000000e+20 |
| 4 |<PHONE_NUMBER>15392384 | 142.589136 | 22.110783 | 0.244439 | -4.941079 | 0.099625 | 0.486224 | 1.000000e+20 |
Python detail: shape
is an attribute, so we can display it’s value without calling it as a function; head
is a function, so we need the parentheses.
Now we can extract the columns we want from gd1_coord
and add them as columns in the DataFrame
. phi1
and phi2
contain the transformed coordinates.
df['phi1'] = gd1_coord.phi1
df['phi2'] = gd1_coord.phi2
df.shape
(140340, 10)
pm_phi1_cosphi2
and pm_phi2
contain the components of proper motion in the transformed frame.
df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2
df['pm_phi2'] = gd1_coord.pm_phi2
df.shape
(140340, 12)
Detail: If you notice that SkyCoord
has an attribute called proper_motion
, you might wonder why we are not using it.
We could have: proper_motion
contains the same data as pm_phi1_cosphi2
and pm_phi2
, but in a different format.
Plot proper motion¶
Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:
In this figure, the shaded area is a high-density region of stars with the proper motion we expect for stars in GD-1.
Due to the nature of tidal streams, we expect the proper motion for most stars to be along the axis of the stream; that is, we expect motion in the direction of
phi2
to be near 0.In the direction of
phi1
, we don’t have a prior expectation for proper motion, except that it should form a cluster at a non-zero value.
To locate this cluster, we’ll select stars near the centerline of GD-1 and plot their proper motion.
Selecting the centerline¶
As we can see in the following figure, many stars in GD-1 are less than 1 degree of declination from the line phi2=0
.
If we select stars near this line, they are more likely to be in GD-1.
We’ll start by selecting the phi2
column from the DataFrame
:
phi2 = df['phi2']
type(phi2)
pandas.core.series.Series
The result is a Series
, which is the structure Pandas uses to represent columns.
We can use a comparison operator, >
, to compare the values in a Series
to a constant.
phi2_min = -1.0 * u.deg
phi2_max = 1.0 * u.deg
mask = (df['phi2'] > phi2_min)
type(mask)
pandas.core.series.Series
mask.dtype
dtype('bool')
The result is a Series
of Boolean values, that is, True
and False
.
mask.head()
0 False
1 False
2 False
3 False
4 False
Name: phi2, dtype: bool
A Boolean Series
is sometimes called a “mask” because we can use it to mask out some of the rows in a DataFrame
and select the rest, like this:
selected = df[mask]
type(selected)
pandas.core.frame.DataFrame
selected
is a DataFrame
that contains only the rows from df
that correspond to True
values in mask
.
The previous mask selects all stars where phi2
exceeds phi2_min
; now we’ll select stars where phi2
falls between phi2_min
and phi2_max
.
phi_mask = ((df['phi2'] > phi2_min) &
(df['phi2'] < phi2_max))
The &
operator computes “logical AND”, which means the result is true where elements from both Boolean Series
are true.
The sum of a Boolean Series
is the number of True
values, so we can use sum
to see how many stars are in the selected region.
phi_mask.sum()
25084
And we can use phi1_mask
to select stars near the centerline, which are more likely to be in GD-1.
centerline = df[phi_mask]
len(centerline)
25084
Here’s a scatter plot of proper motion for the selected stars.
pm1 = centerline['pm_phi1']
pm2 = centerline['pm_phi2']
plt.plot(pm1, pm2, 'ko', markersize=0.1, alpha=0.1)
plt.xlabel('Proper motion phi1 (GD1 frame)')
plt.ylabel('Proper motion phi2 (GD1 frame)');
Looking at these results, we see a large cluster around (0, 0), and a smaller cluster near (0, -10).
We can use xlim
and ylim
to set the limits on the axes and zoom in on the region near (0, 0).
pm1 = centerline['pm_phi1']
pm2 = centerline['pm_phi2']
plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
plt.xlabel('Proper motion phi1 (GD1 frame)')
plt.ylabel('Proper motion phi2 (GD1 frame)')
plt.xlim(-12, 8)
plt.ylim(-10, 10);
Now we can see the smaller cluster more clearly.
You might notice that our figure is less dense than the one in the paper. That’s because we started with a set of stars from a relatively small region. The figure in the paper is based on a region about 10 times bigger.
In the next lesson we’ll go back and select stars from a larger region. But first we’ll use the proper motion data to identify stars likely to be in GD-1.
Filtering based on proper motion¶
The next step is to select stars in the “overdense” region of proper motion, which are candidates to be in GD-1.
In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.
We’ll use a simple rectangle for now, but in a later lesson we’ll see how to select a polygonal region as well.
Here are bounds on proper motion we chose by eye,
pm1_min = -8.9
pm1_max = -6.9
pm2_min = -2.2
pm2_max = 1.0
To draw these bounds, we’ll make two lists containing the coordinates of the corners of the rectangle.
pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr
pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr
Here’s what the plot looks like with the bounds we chose.
plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)
plt.plot(pm1_rect, pm2_rect, '-')
plt.xlabel('Proper motion phi1 (GD1 frame)')
plt.ylabel('Proper motion phi2 (GD1 frame)')
plt.xlim(-12, 8)
plt.ylim(-10, 10);
To select rows that fall within these bounds, we’ll use the following function, which uses Pandas operators to make a mask that selects rows where series
falls between low
and high
.
def between(series, low, high):
"""Make a Boolean Series.
series: Pandas Series
low: lower bound
high: upper bound
returns: Boolean Series
"""
return (series > low) & (series < high)
The following mask select stars with proper motion in the region we chose.
pm_mask = (between(df['pm_phi1'], pm1_min, pm1_max) &
between(df['pm_phi2'], pm2_min, pm2_max))
Again, the sum of a Boolean series is the number of True
values.
pm_mask.sum()
1049
Now we can use this mask to select rows from df
.
selected = df[pm_mask]
len(selected)
1049
These are the stars we think are likely to be in GD-1. Let’s see what they look like, plotting their coordinates (not their proper motion).
phi1 = selected['phi1']
phi2 = selected['phi2']
plt.plot(phi1, phi2, 'ko', markersize=0.5, alpha=0.5)
plt.xlabel('ra (degree GD1)')
plt.ylabel('dec (degree GD1)');
Now that’s starting to look like a tidal stream!
Saving the DataFrame¶
At this point we have run a successful query and cleaned up the results; this is a good time to save the data.
To save a Pandas DataFrame
, one option is to convert it to an Astropy Table
, like this:
selected_table = Table.from_pandas(selected)
type(selected_table)
astropy.table.table.Table
Then we could write the Table
to a FITS file, as we did in the previous lesson.
But Pandas provides functions to write DataFrames in other formats; to see what they are find the functions here that begin with to_
.
One of the best options is HDF5, which is Version 5 of Hierarchical Data Format.
HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).
An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset. (Multi-extension FITS files can also contain more than one table.)
And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).
Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS).
Before we write the HDF5, let’s delete the old one, if it exists.
!rm -f gd1_dataframe.hdf5
We can write a Pandas DataFrame
to an HDF5 file like this:
filename = 'gd1_dataframe.hdf5'
df.to_hdf(filename, 'df')
Because an HDF5 file can contain more than one Dataset, we have to provide a name, or “key”, that identifies the Dataset in the file.
We could use any string as the key, but in this example I use the variable name df
.
Exercise: We’re going to need centerline
and selected
later as well. Write a line or two of code to add it as a second Dataset in the HDF5 file.
# Solution
centerline.to_hdf(filename, 'centerline')
selected.to_hdf(filename, 'selected')
Detail: Reading and writing HDF5 tables requires a library called PyTables
that is not always installed with Pandas. You can install it with pip like this:
pip install tables
If you install it using Conda, the name of the package is pytables
.
conda install pytables
We can use ls
to confirm that the file exists and check the size:
!ls -lh gd1_dataframe.hdf5
-rw-rw-r-- 1 downey downey 17M Oct 19 12:05 gd1_dataframe.hdf5
If you are using Windows, ls
might not work; in that case, try:
!dir gd1_dataframe.hdf5
We can read the file back like this:
read_back_df = pd.read_hdf(filename, 'df')
read_back_df.shape
(140340, 12)
Pandas can write a variety of other formats, which you can read about here.
Summary¶
In this lesson, we re-loaded the Gaia data we saved from a previous query.
We transformed the coordinates and proper motion from ICRS to a frame aligned with GD-1, and stored the results in a Pandas DataFrame
.
Then we replicated the selection process from the Price-Whelan and Bonaca paper:
We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.
We identified a region of proper motion that contains stars likely to be in GD-1.
We used a Boolean
Series
as a mask to select stars whose proper motion is in that region.
So far, we have used data from a relatively small region of the sky. In the next lesson, we’ll write a query that selects stars based on proper motion, which will allow us to explore a larger region.
Best practices¶
When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.
For simple scatter plots in Matplotlib,
plot
is faster thanscatter
.An Astropy
Table
and a PandasDataFrame
are similar in many ways and they provide many of the same functions. They have pros and cons, but for many projects, either one would be a reasonable choice.


1 Rhodopis came to Egypt to work, brought by Xanthes of Samos, but upon her arrival was freed for a lot of money by Kharaxus of Mytilene, son of Scamandronymus and brother of Sappho the poetess.
2 Thus Rhodopis lived as a free woman in Egypt, where, as she was very alluring, she acquired a lot of money—sufficient for such a Rhodopis, so to speak, but not for such a pyramid.
3 Seeing that to this day anyone who likes can calculate what one tenth of her worth was, she cannot be credited with great wealth. For Rhodopis desired to leave a memorial of herself in Greece, by having something made which no one else had thought of or dedicated in a temple and presenting this at Delphi to preserve her memory;
4 so she spent one tenth of her substance on the manufacture of a great number of iron beef spits, as many as the tenth would pay for, and sent them to Delphi; these lie in a heap to this day, behind the altar set up by the Chians and in front of the shrine itself.
5 The courtesans of Naucratis seem to be peculiarly alluring, for the woman of whom this story is told became so famous that every Greek knew the name of Rhodopis, and later on a certain Archidice was the theme of song throughout Greece, although less celebrated than the other.
6 Kharaxus, after giving Rhodopis her freedom, returned to Mytilene. He is bitterly attacked by Sappho in one of her poems. This is enough about Rhodopis.


This test ensures that we can click to focus an a element. Click on the element below.
Focusable anchor with tab index
Focusable anchor
Result
FAIL


--- layout: post status: publish published: true title: Fix for XAML Highlighting in VS2010 author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 1315 wordpress_url: http://winterdom.com/2010/02/fix-for-xaml-highlighting-in-vs2010 date: '2010-02-20 01:18:55 +0000' date_gmt: '2010-02-20 01:18:55 +0000' categories: - Visual Studio tags: [] comments: - id: 2716 author: Updating Visual Studio Extensions // Winterdom author_email: '' author_url: http://winterdom.com/2012/07/updating-visual-studio-extensions date: '2012-07-01 16:54:12 +0000' date_gmt: '2012-07-01 16:54:12 +0000' content: "[...] the plus side, I discovered that my XAML Classifier Fix extension is no longer needed in Visual Studio 2012, now that the team introduced an explicit XAML [...]" ---
Both Visual Studio 2008 and 2010 have a rather annoying issue relating to how XAML is highlighted in the Text Editor. I’ve complained in the past that the XAML designer team introduced a separate set of classifications (i.e. options in the Fonts and Colors dialog) to configure how XAML code would be highlighted by the editor, when in fact, it is just, for the most part XML and it would’ve been used if the XAML editor just used the same color definitions used by the XML editor.
However, this isn’t 100% true. It turns out that the XAML code editor uses the colors and font settings configured for String (e.g. like a C# string) to render raw text inside an XML element in the XAML code. Normally, this isn’t too much of a problem, until you create a color scheme that uses a custom background color to highlight strings. Then, this is what you end up with in XAML:
Notice how the different background is used to render even spaces used for indentation, which looks very, very ugly.
Now, there’s nothing I can do about VS2008, but at least with VS2010, I was able to write an extension that fixes this problem. Here’s how it looks with the extension enabled:
Much better, right?
Here’s basically all the code needed to fix this:
[Export(typeof(IWpfTextViewCreationListener))]
[ContentType("XAML")]
[Name("XAML Fix")]
[TextViewRole(PredefinedTextViewRoles.Document)]
public class XamlFixCreationListener : IWpfTextViewCreationListener {
[Import]
IClassificationTypeRegistryService registry = null;
[Import]
IClassificationFormatMapService mapService = null;
public void TextViewCreated(IWpfTextView textView) {
var formatMap = mapService.GetClassificationFormatMap(textView);
var textClassification = registry.GetClassificationType("text");
var stringClassification = registry.GetClassificationType("string");
var props = formatMap.GetExplicitTextProperties(textClassification);
formatMap.SetExplicitTextProperties(stringClassification, props);
}
}
As usual, you can get the code for the extension from github. Enjoy!


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Woman denies fundraising fraud charges
by Jane HuntThe trial of a woman accused of conning a hospice out of money she claimed she was raising for them will take place next year.
Before Ipswich Crown Court for a plea and trial preparation hearing on Thursday (November 21) was Gina Grimes, 36, of, Findlay Street, Rosythe, Fife.
She pleaded not guilty to two offences of fraud.
The first charge, which is said to have been committed on or about April 16, 2017, relates to an alleged false representation to St Helena Hospice in Colchester that she would give funds raised from an Easter Egg Hunt to the hospice.
The second charge, which is said to have been committed between April 30, 2017 and September 15, 2017, relates to an alleged false representation to someone from St Helena Hospice that she would give money raised from pub quizzes to the hospice and Help for Heroes Charity.
Grimes' trial, which is expected to last four days, will get underway on April 6 next year.
Grimes is on unconditional bail.


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Save $500 on Razer Phone 2 in Black Friday 2019 Week - Now just $299 for Limited Time
If you are a gamer of any sort, then you are pretty much acquainted with the Razer Phone 2. From its flat design to the buttery smooth display, it's packed with everything a gamer would need to get some high scores on the go. And you too can dive into action by saving $500 and grab the smartphone for a price of just $299 today. We're not making this up at all. This is a real deal which is going to last for a limited period of time only.
For those who need a quick recap about the Razer Phone 2, here's what you can expect after spending $299:
120Hz UltraMotion™ Display
Qualcomm Snapdragon 845
Dual Cameras with Image Stabilization
Wireless Fast Charging
IP67 Water Resistance
4,000 mAh battery
1 Year Limited Warranty
The Razer phone will work with both GSM and Verizon LTE networks
We're certain that the description can't get any more apt than this. But, the description does fail to mention that this beast of a phone feature dual front firing speakers which are the best in the business in terms of sound quality.
Let's cut the mustard and dive straight into the deal, shall we?
Buy Razer Phone 2 (New): Unlocked Gaming Smartphone - Was $799, now just $299
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[1]
door , its children still up , was unresponsive to my banging on our common door .
[2]
rooms , and we arrived on the doorsteps of the hard rock , and a room .


{% extends '../../../layouts/dashboard.html' %} {% block content %}
Example block-level help text here.


Exceptional Exceptions
11 June 2016
Creating an exception in Java is a very slow operation. Expect that throwing an exception will cost you around 1-5 microseconds. Nearly all this time is spent on filling in the exception thread stack. The deeper the stack trace is, the more time it will take to populate it. If you take a peek on java.lang.Throwable
source, you will see the answer staring into your face.
public Throwable() {
fillInStackTrace();
}
public synchronized Throwable fillInStackTrace() {
if (stackTrace != null ||
backtrace != null /* Out of protocol state */ ) {
fillInStackTrace(0);
stackTrace = UNASSIGNED_STACK;
}
return this;
}
private native Throwable fillInStackTrace(int dummy);
Usually we throw an exception in case of unexpected problems. This means that we don’t use exceptions as part of the the repetitive if-then-else mess.
Creating the project
$ mvn archetype:generate \
-DinteractiveMode=false \
-DarchetypeGroupId=org.openjdk.jmh \
-DarchetypeArtifactId=jmh-java-benchmark-archetype \
-DgroupId=com.prototype \
-DartifactId=benchmark \
-Dversion=1.0
Writing the benchmark
package com.prototype;
import java.util.concurrent.TimeUnit;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.Mode;
import org.openjdk.jmh.annotations.OutputTimeUnit;
import org.openjdk.jmh.annotations.Warmup;
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@Warmup(iterations = 5, time = 1, timeUnit = TimeUnit.MICROSECONDS)
public class ExceptionBenchmark {
@Benchmark
public Object baseline() {
return new Object();
}
@Benchmark
public Object exceptional() {
return new RuntimeException();
}
}
Compiling
$ mvn clean install
[INFO] Scanning for projects...
[INFO]
[INFO] ------------------------------------------------------------------------
[INFO] Building JMH benchmark sample: Java 1.0
[INFO] ------------------------------------------------------------------------
[INFO]
[INFO] --- maven-clean-plugin:2.5:clean (default-clean) @ benchmark ---
[INFO] Deleting /home/drmanalo/workspace/benchmark/target
[INFO]
[INFO] --- maven-resources-plugin:2.6:resources (default-resources) @ benchmark ---
[INFO] Using 'UTF-8' encoding to copy filtered resources.
[INFO] skip non existing resourceDirectory /home/drmanalo/workspace/benchmark/src/main/resources
[INFO]
[INFO] --- maven-compiler-plugin:3.1:compile (default-compile) @ benchmark ---
[INFO] Changes detected - recompiling the module!
[INFO] Compiling 1 source file to /home/drmanalo/workspace/benchmark/target/classes
[INFO]
[INFO] --- maven-resources-plugin:2.6:testResources (default-testResources) @ benchmark ---
[INFO] Using 'UTF-8' encoding to copy filtered resources.
[INFO] skip non existing resourceDirectory /home/drmanalo/workspace/benchmark/src/test/resources
[INFO]
[INFO] --- maven-compiler-plugin:3.1:testCompile (default-testCompile) @ benchmark ---
[INFO] No sources to compile
[INFO]
[INFO] --- maven-surefire-plugin:2.17:test (default-test) @ benchmark ---
[INFO] No tests to run.
[INFO]
[INFO] --- maven-jar-plugin:2.4:jar (default-jar) @ benchmark ---
[INFO] Building jar: /home/drmanalo/workspace/benchmark/target/benchmark-1.0.jar
[INFO]
[INFO] --- maven-shade-plugin:2.2:shade (default) @ benchmark ---
[INFO] Including org.openjdk.jmh:jmh-core:jar:1.12 in the shaded jar.
[INFO] Including net.sf.jopt-simple:jopt-simple:jar:4.6 in the shaded jar.
[INFO] Including org.apache.commons:commons-math3:jar:3.2 in the shaded jar.
[INFO] Replacing /home/drmanalo/workspace/benchmark/target/benchmarks.jar with /home/drmanalo/workspace/benchmark/target/benchmark-1.0-shaded.jar
[INFO]
[INFO] --- maven-install-plugin:2.5.1:install (default-install) @ benchmark ---
[INFO] Installing /home/drmanalo/workspace/benchmark/target/benchmark-1.0.jar to /home/drmanalo/.m2/repository/com/prototype/benchmark/1.0/benchmark-1.0.jar
[INFO] Installing /home/drmanalo/workspace/benchmark/pom.xml to /home/drmanalo/.m2/repository/com/prototype/benchmark/1.0/benchmark-1.0.pom
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 2.604 s
[INFO] Finished at: 2016-06-21T23:03:56+01:00
[INFO] Final Memory: 24M/203M
[INFO] ------------------------------------------------------------------------
Running the benchmarks
$ java -jar target/benchmarks.jar
# JMH 1.12 (released 82 days ago)
# VM version: JDK 1.8.0_91, VM 25.91-b14
# VM invoker: /opt/jdk1.8.0_91/jre/bin/java
# VM options: <none>
# Warmup: 5 iterations, 1 us each
# Measurement: 20 iterations, 1 s each
# Timeout: 10 min per iteration
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Average time, time/op
# Benchmark: com.prototype.ExceptionBenchmark.baseline
# Run progress: 0.00% complete, ETA 00:06:40
# Fork: 1 of 10
# Warmup Iteration 1: 0.243 us/op
# Warmup Iteration 2: 0.038 us/op
# Warmup Iteration 3: 0.011 us/op
# Warmup Iteration 4: 0.016 us/op
# Warmup Iteration 5: 0.004 us/op
Iteration 1: 0.004 us/op
Iteration 2: 0.004 us/op
Iteration 3: 0.004 us/op
Iteration 4: 0.004 us/op
Iteration 5: 0.004 us/op
Iteration 6: 0.004 us/op
Iteration 7: 0.004 us/op
Iteration 8: 0.004 us/op
Iteration 9: 0.004 us/op
Iteration 10: 0.004 us/op
Iteration 11: 0.004 us/op
Iteration 12: 0.004 us/op
Iteration 13: 0.004 us/op
Iteration 14: 0.004 us/op
Iteration 15: 0.004 us/op
Iteration 16: 0.004 us/op
Iteration 17: 0.004 us/op
Iteration 18: 0.004 us/op
Iteration 19: 0.004 us/op
Iteration 20: 0.004 us/op
# JMH 1.12 (released 82 days ago)
# VM version: JDK 1.8.0_91, VM 25.91-b14
# VM invoker: /opt/jdk1.8.0_91/jre/bin/java
# VM options: <none>
# Warmup: 5 iterations, 1 us each
# Measurement: 20 iterations, 1 s each
# Timeout: 10 min per iteration
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Average time, time/op
# Benchmark: com.prototype.ExceptionBenchmark.exceptional
# Run progress: 50.00% complete, ETA 00:03:29
# Fork: 1 of 10
# Warmup Iteration 1: 2.083 us/op
# Warmup Iteration 2: 2.454 us/op
# Warmup Iteration 3: 1.918 us/op
# Warmup Iteration 4: 1.853 us/op
# Warmup Iteration 5: 1.455 us/op
Iteration 1: 1.096 us/op
Iteration 2: 1.067 us/op
Iteration 3: 1.073 us/op
Iteration 4: 1.075 us/op
Iteration 5: 1.069 us/op
Iteration 6: 1.074 us/op
Iteration 7: 1.075 us/op
Iteration 8: 1.065 us/op
Iteration 9: 1.074 us/op
Iteration 10: 1.070 us/op
Iteration 11: 1.075 us/op
Iteration 12: 0.994 us/op
Iteration 13: 0.991 us/op
Iteration 14: 0.994 us/op
Iteration 15: 1.003 us/op
Iteration 16: 0.999 us/op
Iteration 17: 1.006 us/op
Iteration 18: 1.001 us/op
Iteration 19: 0.999 us/op
Iteration 20: 0.998 us/op
and the test results...


Color
Collision
FOCUS
is the weapon. Don't make the wrong move. Tap / click to change the color and save the central balls.  Better play
SAFE
and make a new highscore.
PLAY
Game Over!
RETRY


This logo incorperates the mentality and comtemporary nature through the sharp logo design. The elements are drawn from computer system symbols- the bluetooth icon and >. These symbols represent the modern and innovative image of the company. The ‘b’ in the center was inspired by the blue- tooth icon and created by the intersection of two right arrows, interspersed with negative space to give it a third dimention. This third dimention reperesents the new direction that Back- door Records is taking their artists in record producing. Ad- ditionally, the layered blocks present the stage Backdoor re- cords witll take the artists to, one way or another The clean and simple logo manifests the modern feel and pro- gressive future of the young company. The black and white keeps the image bold, yet universal, so as to incoperate all genres of music the record company may work with. The sharp edges maintain the new age look while still keeping the circle and strong look of the previous logo.


Class: Aws::QuickSight::Types::SheetDefinition
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb |
Overview
A sheet is an object that contains a set of visuals that are viewed together on one page in a paginated report. Every analysis and dashboard must contain at least one sheet.
Constant Summary
Instance Attribute Summary
-
#content_type ⇒ String
rw
The layout content type of the sheet.
-
#description ⇒ String
rw
A description of the sheet.
-
#filter_controls ⇒ Array<Types::FilterControl>
rw
The list of filter controls that are on a sheet.
-
#layouts ⇒ Array<Types::Layout>
rw
Layouts define how the components of a sheet are arranged.
-
#name ⇒ String
rw
The name of the sheet.
-
#parameter_controls ⇒ Array<Types::ParameterControl>
rw
The list of parameter controls that are on a sheet.
-
#sheet_control_layouts ⇒ Array<Types::SheetControlLayout>
rw
The control layouts of the sheet.
-
#sheet_id ⇒ String
rw
The unique identifier of a sheet.
-
#text_boxes ⇒ Array<Types::SheetTextBox>
rw
The text boxes that are on a sheet.
-
#title ⇒ String
rw
The title of the sheet.
-
#visuals ⇒ Array<Types::Visual>
rw
A list of the visuals that are on a sheet.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#content_type ⇒ String
(rw)
The layout content type of the sheet. Choose one of the following options:
PAGINATED
: Creates a sheet for a paginated report.INTERACTIVE
: Creates a sheet for an interactive dashboard.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#description ⇒ String
(rw)
A description of the sheet.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#filter_controls ⇒ Array
<Types::FilterControl> (rw)
The list of filter controls that are on a sheet.
For more information, see Adding filter controls to analysis
sheets in the Amazon ::Aws::QuickSight
User Guide.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#layouts ⇒ Array
<Types::Layout> (rw)
Layouts define how the components of a sheet are arranged.
For more information, see Types of layout in the Amazon
::Aws::QuickSight
User Guide.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#name ⇒ String
(rw)
The name of the sheet. This name is displayed on the sheet's tab in the Amazon QuickSight console.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#parameter_controls ⇒ Array
<Types::ParameterControl> (rw)
The list of parameter controls that are on a sheet.
For more information, see Using a Control with a Parameter in
Amazon QuickSight in the Amazon ::Aws::QuickSight
User Guide.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#sheet_control_layouts ⇒ Array
<Types::SheetControlLayout> (rw)
The control layouts of the sheet.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#sheet_id ⇒ String
(rw)
The unique identifier of a sheet.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#text_boxes ⇒ Array
<Types::SheetTextBox> (rw)
The text boxes that are on a sheet.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#title ⇒ String
(rw)
The title of the sheet.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end
#visuals ⇒ Array
<Types::Visual> (rw)
A list of the visuals that are on a sheet. Visual
placement is
determined by the layout of the sheet.
# File 'gems/aws-sdk-quicksight/lib/aws-sdk-quicksight/types.rb', line 24011
class SheetDefinition < Struct.new( :sheet_id, :title, :description, :name, :parameter_controls, :filter_controls, :visuals, :text_boxes, :layouts, :sheet_control_layouts, :content_type) SENSITIVE = [] include Aws::Structure end